Optimization of differentiated learning through training on innovative learning activities using Loose Parts
Bibliographic record
Abstract
The new paradigm of the Merdeka Curriculum emphasizes the implementation of differentiated learning, aiming to provide enjoyable learning experiences tailored to students' knowledge levels and learning needs. This Community Service Program is designed to train early childhood education (PAUD) teachers to adapt and innovate in Differentiated Learning using Loose Parts. The program targets PAUD teachers at the Cluster Activity Center (PKG) in Jelbuk District, Jember Regency. The participating community comprises 17 PAUD institutions, with a total of 30 PAUD teachers involved. The program is conducted in three stages: socialization, training, and best practice mentoring. Methods employed include lectures, quizzes, and project-based activities. Based on participant satisfaction questionnaires, the results of this community service initiative reveal high satisfaction scores: 96.67 percent in the attitude aspect, 90 percent in the knowledge aspect, and 90 percent in the skills aspect. Furthermore, through best practice activities, teachers achieved optimal knowledge and skill transfer. This will enable them to innovate Differentiated Learning activities using Loose Parts, which can be implemented in the learning activities of their respective PAUD institutions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".